Socure, Inc.

Pablo Ysrrael Abreu

Ali Haddad

Omar Gutierrez

Stanislav Palatnik

Lukas Dylan Osborne

Eric Levine

Elliot John Hughes

Hayden Edelson

Edward Li

Vijin K P Parambath

Badri Nagabandi

Stansilav Palatnik

Feng Xiao

Provided is a system and methodology which enhances the readability of barcodes using machine learning techniques wherein a model is trained using exemplary data and the resulting model is used in production to increase the chances of successfully capturing the data in a barcode notwithstanding that the barcode may be of low quality. According to the teachings of the present invention, the system is trained using large quantities of synthetic personal information associated with barcodes. Once the system is placed into production, the trained model is applied as against newly identified barcodes to enhance the readability of the original barcode based on features associated with the trained model.

Generative system and method for enhancing readability of barcodes using frequency guided computer vision

Provided are a system and methodology for iteratively measuring data, as between multiple sets thereof, that accounts for underlying data generation sources and bases. Doing so, via normalization of the data, enables uniformity of interpretation and presentation of the data no matter the machine learning model that produced the data.

Self learning machine learning transaction scores adjustment via normalization thereof accounting for underlying transaction score bases relating to an occurrence of fraud in a transaction

Provided are a system and methodology for iteratively transforming data, as between multiple sets thereof, that account for underlying data generation sources and bases. Doing so, via normalization of the data, enables uniformity of interpretation and presentation of the data no matter the machine learning model that produced the data.

Self learning machine learning transaction scores adjustment via normalization thereof accounting for underlying transaction score bases

Provided are a system and methodology for iteratively transforming data as between multiple sets thereof. Doing so, via normalization of the data, enables uniformity of interpretation and presentation of the data no matter the machine learning model that produced the data.

Self learning machine learning transaction scores adjustment via normalization thereof

The system and methodology of the present invention employs novel machine learning techniques in order to periodically update and supplement a set of identity verification databases used in connection with KYC determinations. The system and methodology of the present invention periodically receives updated identity data from third-party sources and may also use customer logic input provided to update the model used in making KYC determinations. The system of the present invention further updates and optimizes the identity verification databases and selectively deploys them in a production environment without requiring any human intervention, such that identity verification is incrementally improved over time as more identity data is provided to the system.

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New York, NY, United States of America

Pablo Ysrrael Abreu